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This commit is contained in:
DrSmoothl
2026-03-13 11:45:26 +08:00
parent 2a510312bc
commit a576313b22
70 changed files with 956 additions and 731 deletions
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131
src/maisaka/llm_service.py
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@@ -4,7 +4,6 @@ MaiSaka LLM 服务 - 使用主项目 LLM 系统
"""
import json
import os
from dataclasses import dataclass
from typing import List, Optional, Literal
@@ -34,6 +33,7 @@ MSG_TYPE_FIELD = "_type"
@dataclass
class ToolCall:
"""工具调用信息"""
id: str
name: str
arguments: dict
@@ -42,6 +42,7 @@ class ToolCall:
@dataclass
class ChatResponse:
"""LLM 对话循环单步响应"""
content: Optional[str]
tool_calls: List[ToolCall]
raw_message: dict # 可直接追加到对话历史的消息字典
@@ -49,6 +50,7 @@ class ChatResponse:
# ──────────────────── 工具函数 ────────────────────
def build_message(role: str, content: str, msg_type: MessageType = "user", **kwargs) -> dict:
"""构建消息字典，包含消息类型标记。"""
msg = {"role": role, "content": content, MSG_TYPE_FIELD: msg_type, **kwargs}
@@ -93,23 +95,18 @@ class MaiSakaLLMService:
except Exception:
# 如果配置加载失败，使用默认配置
from src.config.model_configs import ModelTaskConfig
self._model_configs = ModelTaskConfig()
logger.warning("无法加载主项目模型配置，使用默认配置")
# 初始化 LLMRequest 实例（只使用 tool_use 和 replyer）
self._llm_tool_use = LLMRequest(
model_set=self._model_configs.tool_use,
request_type="maisaka_tool_use"
)
self._llm_tool_use = LLMRequest(model_set=self._model_configs.tool_use, request_type="maisaka_tool_use")
# 主对话也使用 tool_use 模型（因为需要工具调用支持）
self._llm_chat = self._llm_tool_use
# 分析模块也使用 tool_use 模型
self._llm_utils = self._llm_tool_use
# 回复生成使用 replyer 模型
self._llm_replyer = LLMRequest(
model_set=self._model_configs.replyer,
request_type="maisaka_replyer"
)
self._llm_replyer = LLMRequest(model_set=self._model_configs.replyer, request_type="maisaka_replyer")
# 尝试修复数据库 schema（忽略错误）
self._try_fix_database_schema()
@@ -133,6 +130,7 @@ class MaiSakaLLMService:
chat_prompt.add_context("file_tools_section", tools_section if tools_section else "")
import asyncio
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
@@ -147,7 +145,9 @@ class MaiSakaLLMService:
self._chat_system_prompt = chat_system_prompt
# 获取模型名称用于显示
self._model_name = self._model_configs.tool_use.model_list[0] if self._model_configs.tool_use.model_list else "未配置"
self._model_name = (
self._model_configs.tool_use.model_list[0] if self._model_configs.tool_use.model_list else "未配置"
)
# 加载子模块提示词
self._emotion_prompt: Optional[str] = None
@@ -157,21 +157,22 @@ class MaiSakaLLMService:
try:
import asyncio
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
self._emotion_prompt = loop.run_until_complete(prompt_manager.render_prompt(
prompt_manager.get_prompt("maidairy_emotion")
))
self._cognition_prompt = loop.run_until_complete(prompt_manager.render_prompt(
prompt_manager.get_prompt("maidairy_cognition")
))
self._timing_prompt = loop.run_until_complete(prompt_manager.render_prompt(
prompt_manager.get_prompt("maidairy_timing")
))
self._context_summarize_prompt = loop.run_until_complete(prompt_manager.render_prompt(
prompt_manager.get_prompt("maidairy_context_summarize")
))
self._emotion_prompt = loop.run_until_complete(
prompt_manager.render_prompt(prompt_manager.get_prompt("maidairy_emotion"))
)
self._cognition_prompt = loop.run_until_complete(
prompt_manager.render_prompt(prompt_manager.get_prompt("maidairy_cognition"))
)
self._timing_prompt = loop.run_until_complete(
prompt_manager.render_prompt(prompt_manager.get_prompt("maidairy_timing"))
)
self._context_summarize_prompt = loop.run_until_complete(
prompt_manager.render_prompt(prompt_manager.get_prompt("maidairy_context_summarize"))
)
logger.info("成功加载 MaiSaka 子模块提示词")
finally:
loop.close()
@@ -191,9 +192,7 @@ class MaiSakaLLMService:
if "model_api_provider_name" not in columns:
# 添加缺失的列
session.execute(text(
"ALTER TABLE llm_usage ADD COLUMN model_api_provider_name VARCHAR(255)"
))
session.execute(text("ALTER TABLE llm_usage ADD COLUMN model_api_provider_name VARCHAR(255)"))
session.commit()
logger.info("数据库 schema 已修复：添加 model_api_provider_name 列")
except Exception:
@@ -205,7 +204,7 @@ class MaiSakaLLMService:
self._extra_tools = list(tools)
@staticmethod
def _tool_option_to_dict(tool: 'ToolOption') -> dict:
def _tool_option_to_dict(tool: "ToolOption") -> dict:
"""将 ToolOption 对象转换为主项目期望的 dict 格式
主项目的 _build_tool_options() 期望的格式:
@@ -218,18 +217,8 @@ class MaiSakaLLMService:
params = []
if tool.params:
for param in tool.params:
params.append((
param.name,
param.param_type,
param.description,
param.required,
param.enum_values
))
return {
"name": tool.name,
"description": tool.description,
"parameters": params
}
params.append((param.name, param.param_type, param.description, param.required, param.enum_values))
return {"name": tool.name, "description": tool.description, "parameters": params}
async def chat_loop_step(self, chat_history: List[dict]) -> ChatResponse:
"""执行对话循环的一步 - 使用 tool_use 模型"""
@@ -271,11 +260,13 @@ class MaiSakaLLMService:
for tc in msg["tool_calls"]:
tc_func = tc.get("function", {})
# 主项目的 ToolCall: call_id, func_name, args
tool_calls_list.append(ToolCallOption(
call_id=tc.get("id", ""),
func_name=tc_func.get("name", ""),
args=json.loads(tc_func.get("arguments", "{}")) if tc_func.get("arguments") else {}
))
tool_calls_list.append(
ToolCallOption(
call_id=tc.get("id", ""),
func_name=tc_func.get("name", ""),
args=json.loads(tc_func.get("arguments", "{}")) if tc_func.get("arguments") else {},
)
)
builder.set_tool_calls(tool_calls_list)
elif role == "tool" and "tool_call_id" in msg:
builder.add_tool_call(msg["tool_call_id"])
@@ -290,15 +281,17 @@ class MaiSakaLLMService:
# 调用 LLM（使用带消息的接口）
# 合并内置工具和额外工具（将 ToolOption 对象转换为 dict）
all_tools = [self._tool_option_to_dict(t) for t in get_builtin_tools()] + (self._extra_tools if self._extra_tools else [])
all_tools = [self._tool_option_to_dict(t) for t in get_builtin_tools()] + (
self._extra_tools if self._extra_tools else []
)
# 打印消息列表
built_messages = message_factory(None)
print("\n" + "="*60)
print("\n" + "=" * 60)
print("MaiSaka LLM Request - chat_loop_step:")
for msg in built_messages:
print(f" {msg}")
print("="*60 + "\n")
print("=" * 60 + "\n")
response, (reasoning, model, tool_calls) = await self._llm_chat.generate_response_with_message_async(
message_factory=message_factory,
@@ -312,15 +305,17 @@ class MaiSakaLLMService:
if tool_calls:
for tc in tool_calls:
# 主项目的 ToolCall 有 call_id, func_name, args
call_id = tc.call_id if hasattr(tc, 'call_id') else ""
func_name = tc.func_name if hasattr(tc, 'func_name') else ""
args = tc.args if hasattr(tc, 'args') else {}
call_id = tc.call_id if hasattr(tc, "call_id") else ""
func_name = tc.func_name if hasattr(tc, "func_name") else ""
args = tc.args if hasattr(tc, "args") else {}
converted_tool_calls.append(ToolCall(
id=call_id,
name=func_name,
arguments=args,
))
converted_tool_calls.append(
ToolCall(
id=call_id,
name=func_name,
arguments=args,
)
)
# 构建原始消息格式（MaiSaka 风格）
raw_message = {"role": "assistant", "content": response}
@@ -394,10 +389,10 @@ class MaiSakaLLMService:
prompt = "\n".join(prompt_parts)
print("\n" + "="*60)
print("\n" + "=" * 60)
print("MaiSaka LLM Request - analyze_emotion:")
print(f" {prompt}")
print("="*60 + "\n")
print("=" * 60 + "\n")
try:
response, _ = await self._llm_utils.generate_response_async(
@@ -428,10 +423,10 @@ class MaiSakaLLMService:
prompt = "\n".join(prompt_parts)
print("\n" + "="*60)
print("\n" + "=" * 60)
print("MaiSaka LLM Request - analyze_cognition:")
print(f" {prompt}")
print("="*60 + "\n")
print("=" * 60 + "\n")
try:
response, _ = await self._llm_utils.generate_response_async(
@@ -463,10 +458,10 @@ class MaiSakaLLMService:
prompt = "\n".join(prompt_parts)
print("\n" + "="*60)
print("\n" + "=" * 60)
print("MaiSaka LLM Request - analyze_timing:")
print(f" {prompt}")
print("="*60 + "\n")
print("=" * 60 + "\n")
try:
response, _ = await self._llm_utils.generate_response_async(
@@ -498,10 +493,10 @@ class MaiSakaLLMService:
prompt = "\n".join(prompt_parts)
print("\n" + "="*60)
print("\n" + "=" * 60)
print("MaiSaka LLM Request - summarize_context:")
print(f" {prompt}")
print("="*60 + "\n")
print("=" * 60 + "\n")
try:
response, _ = await self._llm_utils.generate_response_async(
@@ -529,8 +524,7 @@ class MaiSakaLLMService:
# 格式化对话历史
filtered_history = [
msg for msg in chat_history
if msg.get("role") != "system" and msg.get("_type") != "perception"
msg for msg in chat_history if msg.get("role") != "system" and msg.get("_type") != "perception"
]
formatted_history = format_chat_history(filtered_history)
@@ -542,18 +536,15 @@ class MaiSakaLLMService:
system_prompt = "你是一个友好的 AI 助手，请根据用户的想法生成自然的回复。"
user_prompt = (
f"当前时间：{current_time}\n\n"
f"【聊天记录】\n{formatted_history}\n\n"
f"【你的想法】\n{reason}\n\n"
f"现在，你说："
f"当前时间：{current_time}\n\n【聊天记录】\n{formatted_history}\n\n【你的想法】\n{reason}\n\n现在，你说："
)
messages = f"System: {system_prompt}\n\nUser: {user_prompt}"
print("\n" + "="*60)
print("\n" + "=" * 60)
print("MaiSaka LLM Request - generate_reply:")
print(f" {messages}")
print("="*60 + "\n")
print("=" * 60 + "\n")
try:
response, _ = await self._llm_replyer.generate_response_async(